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  • Introduce what Jupyter is and why it’s useful

  • Identify the difference between Jupyter Lab and Jupyter Notebooks and when to use one tool is better than the otherover another

  • Demonstrate the Jupyter service within OnDemand across a variety of available languages and kernels

  • Demonstrate how to convert an existing Conda environment into a kernel that can be used within a Jupyter session

Note

Notes:

  • The workshop modules work best in a sequential manner as a story introducing concepts and providing examples, but sections can be used separately to focus on a particular concept.

  • You will need to modify usernames, project names, and folder locations, to apply to yourself.

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  1. Intro to Jupyter Notebooks vs Labs

  2. Creating a Shared Library of R Packages: Demonstrate how to use an R library to create a shared set of R packages.

  3. Using R and RStudio within OnDemand: Detail the process of using R and RStudio via the OnDemand service.

  4. Using an R Conda Environment with RStudio: Detail how to use an R Conda Environment within RStudio.

  5. Create an R Kernel for a Jupyter Notebook: Detail how to update an R Conda environment so it can be used as a kernel within ARCC’s Jupyter service.

  6. Parallel R: Introduction: Introduction some high-level aspects of using R in parallel relating to the cluster.

  7. Using R/RStudio on the Cluster: Summary: Summarize the concepts covered across the workshop.Using Jupyter in OnDemand

  8. Converting a conda environment into a kernel to use in a Jupyter session

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Link to main workshop page

 

Where are R Packages Installed on the Cluster?